Method for Determining Continuous Information on an Expected Trajectory of an Object

ABSTRACT

Computer-implemented method for determining continuous information on an expected trajectory of an object, the method comprising at least the following steps carried out by computer hardware components: determining data related to an expected trajectory of an object; and determining at least one parameter value for a continuous function on the basis of the data, wherein the continuous function and the at least one parameter value represent continuous information on the expected trajectory of the object.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to European Patent Application Number19219051.0, filed Dec. 20, 2019, the disclosure of which is herebyincorporated by reference in its entirety herein.

TECHNICAL FIELD

The present disclosure relates to a computer-implemented method fordetermining continuous information on an expected trajectory of anobject.

BACKGROUND

Digital imaging devices, such as digital cameras, are used in automotiveapplications to capture the vicinity of a vehicle, for example in a busytraffic environment. Other types of sensors, such as radar or LiDAR, canbe used for the same purpose. However, it is not trivial to determinedesired semantic information from the sensor data or the various typesof sensor data. Particularly, information about the future movement oftraffic objects (e.g., vehicles, bicycles, or pedestrians) is oneexample of such information, which needs to be determined from sensordata. The future or expected trajectory, i.e. path of movement of anobject is relevant in order to automatically control a vehicle in a safemanner. In this regard, it is possible to predict data samples forfuture points in time on the basis of current sensor data samples.However, due to the pointwise or discrete nature of the data samples theresult would be a coarse prediction on the expected movement. This isinsufficient for a precise calculation of safe steering maneuvers at anypoint in time, for example for an autonomous vehicle. Increasing thenumber of predictions per time span could alleviate this problem butraises a conflict with limited processing resources in a vehicle beingcapable to generate predictions at real time.

SUMMARY

The present disclosure provides a computer-implemented method, acomputer system and a non-transitory computer readable medium accordingto the independent claims. Embodiments are given in the subclaims, thedescription and the drawings.

In one aspect, the present disclosure is directed at acomputer-implemented method for determining continuous information on anexpected trajectory of an object, the method comprising at least thefollowing steps carried out by computer hardware components: determiningdata, the data being related to the expected trajectory of the object;and determining at least one parameter value for a continuous functionon the basis of the data, wherein the continuous function and the atleast one parameter value represent continuous information on theexpected trajectory of the object, and wherein the at least oneparameter value is determined by using a machine-learning model.

It has been found that continuous information on the expected trajectoryof an object can be provided with a continuous function and at least oneparameter value for the continuous function by using a machine-learningmodel. In this way, the information on the expected trajectory isprovided with respect to a time period or range rather than just a pointor sample in time. This allows for any desired time resolution of theexpected trajectory and a time-dependent behavior of the object isprecisely taken into account. As a result, a precise and safe generationof automatic control functions of a vehicle can be carried out on thebasis of the determined information for any required point in time. Thismeans that less precise assumptions about the expected trajectory on thebasis of only a few separate data samples can now be avoided. Amachine-learning model is capable to provide this information with highvalidity. The machine-learning model can be a mathematical model trainedby a machine-learning algorithm, for example an artificial neuralnetwork or a support-vector machine. The training can be carried out onthe basis of known trajectories of objects, for example usingbackpropagation.

It is preferred that the at least one parameter value is directlydetermined by using the machine-learning model. This means that the oneor more parameter values are preferably the output of themachine-learning model, for example of a neural network. The use ofadditional rules outside the scope of the machine-learning model, forexample additional mathematical models can be excluded from determiningthe parameter value.

The continuous function is a mathematical function that can be expressedin closed form and is well suitable for implementation on a computersystem. It is also possible that only the parameter values aredetermined while the continuous function can be predetermined exceptfrom parameter values for the parameters of the function. In this way,the processing effort for determining the information on the expectedtrajectory is significantly reduced, for example compared to determininga plurality of separate data points of the expected trajectory. The sameholds for confidence information related to the expected trajectory orsimilar information. As another aspect, the accuracy of the expectedtrajectory or the related information is increased compared topredicting separate data points. This is due to the continuous nature ofthe information, which provides information on the expected trajectorywith respect to any desired point in time rather than just sampled, i.e.limited points in time.

The continuous information can generally describe the shape of theexpected trajectory but is not limited thereto. For example, thecontinuous information can represent confidence information of at leastone parameter. In one example, at least one parameter value represents aconfidence value (for example variance) for the continuous function, inparticular for a parameter value of the continuous function.

The term expected trajectory indicates that the object trajectory ispredicted. This is done on the basis of data, which can be sensor datadetermined by the sensor mounted on another object, in particular avehicle located nearby the object for which the trajectory informationis determined. The data is preferably determined at a first time, whichis different to a second time that is assigned to the expectedtrajectory. For example, sensor data can be determined at a first pointof time, e.g. in form of a radar scan, which captures for example therespective object and other possibly interacting objects. It is assumedthat objects will avoid collisions, so their trajectories can bedependent on each other. The radar scan for the second time is notavailable because the second time is preferably a future time. In orderto determine the desired information on the expected trajectory of theobject that is preferably captured by means of the radar scan, it isproposed to determine one or more parameter values for a continuousfunction, wherein the continuous function and the one or more parametervalues together represent the information on the expected trajectory ofthe object. While the second time is preferably a time period subsequentto the first-time other time relations are also possible.

The data related to the expected trajectory can be any data thatpossibly affects the trajectory of the object. The data can be datarepresenting the object, other objects in the vicinity, the environment(for example the road and obstacles). In addition, other environmentaldata can be used, for example the temperature, or other informationabout the environmental conditions, e.g. weather. In one example, duringheavy rain or snow, the expected trajectory might be different comparedto excellent weather conditions. This information can also berepresented as confidence information, as will be described furtherbelow.

While the expected trajectory can refer to self-moving objects, such asvehicles and pedestrians, also stationary objects can be considered. Forexample, due to ego motion of a vehicle objects sensed by a sensor ofthe vehicle are moving relative to the vehicle and a trajectory can bedetermined on the basis of the relative movement, for example atrajectory of a road or another object. In principle, the shape ofobjects, for example the course of a road can be described by thecontinuous information.

It is understood that a plurality of parameter values can be assignedfor the continuous function, which may be denoted a set of parametervalues for the continuous function. For example, sets of parametervalues can be determined for a plurality of continuous functions,respectively. In general, more than one continuous function can beprovided.

Embodiments are disclosed in the description, the dependent claims, andthe drawings.

According to an embodiment, the continuous function represents afunction value on the basis of a continuous variable, preferably acontinuous-time variable, and at least one parameter that is set to theat least one parameter value. The continuous function is preferably areal-valued function. Furthermore, the term continuous function can beunderstood in the sense that the graph of the function does not compriseinterruptions or holes. In more simple terms, the graph can be drawn asa continuous line. In this regard, the continuous variable can be set toan arbitrary real value, preferably within a defined range of thecontinuous function.

The continuous function can be predefined and for example stored in acomputer system. Only the parameter values for the continuous functionneed to be determined, which renders the step of determining thecontinuous information compact. The particular type of continuousfunction can be chosen with respect to several assumptions about theexpected trajectory of a given object. For example, if only a certainrange of maneuvers are to be expected the continuous function can bechosen accordingly. In one example, the continuous function can bechosen so that maneuvers of a passenger car can be modelled adequately.Other functions may be chosen for a trucks, buses, or pedestrians. Inanother example, the same continuous function is chosen for all objects.It is understood that the term continuous function indicates thefunction in a general, in particular mathematical sense, e.g. with avariable and one or more parameters. The parameter values for parametersdefine a particular instance of the continuous function.

According to an embodiment, the at least one parameter value isdetermined by using an artificial neural network. In this way, the oneor more parameter value can be determined with high accuracy and withlow processing effort. Particularly, an artificial neural network iscapable to process a substantial amount of data with high efficiency andaccuracy. In one example, the artificial neural network is aconvolutional neural network. This is suitable for processing complexsensor data, such as radar sensor data or image data. As an alternativeto a neural network other types of machine-learning models or artificialintelligence can also be used for determining the one or more parametervalue for a continuous function.

According to an embodiment, the artificial neural network comprises aplurality of layers, wherein the at least one parameter value comprisesa plurality of parameter values, wherein at least some of the pluralityof parameter values are respectively determined by a respective one ofthe plurality of layers of the artificial neural network. In otherwords, the layer structure of the neural network corresponds to astructure of the parameter values, which can be structured in groups ofparameter values with respect to one or more continuous functions. Inone example, a plurality of sets of parameter values are respectivelyassigned to a respective one of a plurality of continuous functions.This is, a first set of parameter values is assigned to a firstcontinuous function and a second set of parameter values is assigned toa second continuous function, wherein the first and second continuousfunctions can be equivalent of different. The first set of parametervalues can be determined by using a first layer of the neural networkand the second set of parameter values can be determined by using asecond layer of the neural network. Using dedicated layers for theparameter values of continuous functions allows for a compact neuralnetwork, which performs with high efficiency and accuracy. Moreover,manual tuning of individual layers of the neural network can be done ifdesired. Alternatively, at least some or all of the plurality ofparameter values can be determined by a single one of the plurality oflayers of the artificial neural network. Also combinations can be used,i.e. the parameter values for one function are determined by a singlelayer, wherein the parameter values for another function are determinedby using different layers.

According to an embodiment, the continuous function forms a polynomialfunction. Using polynomial functions has shown to provide a suitablemathematical model for determining trajectories of common objects invarious traffic scenarios. Moreover, a polynomial function is able tomodel the expected range of possible trajectories, wherein unlikelytrajectories or maneuvers of the objects are avoided. As another aspect,parameter values for a polynomial function can be determined with veryhigh efficiency. Particularly, algorithms for fitting a polynomialfunction are available in the art. As indicated, a neural network canalso be used. Instead of polynomials, other types of continuousfunctions can be used, for example splines.

According to an embodiment the polynomial function has a degree of atleast one, for example at least two, in one example four. A degree offour can provide a good compromise for modelling a true trajectory withhigh accuracy and efficiency. However, higher degree polynomialfunctions or first-order polynomial functions (degree one) can also beused, if desired.

According to an embodiment, the at least one parameter value comprisesat least one first parameter value and at least one second parametervalue, wherein a first continuous function and the at least one firstparameter value represent the expected trajectory of the object in afirst dimension, wherein a second continuous function and the at leastone second parameter value represent the expected trajectory of theobject in a second dimension. The first and second dimension can bespatial dimensions, for example orthogonal dimensions. Other types ofdimensions are also possible and can be dependent on the spatialrepresentation of objects, for example the type of coordinate system. Inone example, the first and second continuous functions can be associatedwith spatial dimensions having continuous time dependency, e.g., x(t)and y(t), wherein x and y denote for example orthogonal or polar spatialdimensions and t represents continuous time. However, in anotherexample, the second continuous function can be dependent on the firstcontinuous function, e.g. x(t) and y(x). Other forms of dimensions arealso possible. In particular, a dimension can be a velocity-relateddimension, for example velocity or acceleration. A dimension can alsorepresent a statistical dimension, for example confidence, for examplevariance. In principle, a dimension can be related to any desiredmeasurand.

It is understood that any desired number of continuous functions can beused and that the parameter values can refer to any desired number ofcontinuous functions. For example, more than two continuous functionscan be used for determining the continuous information.

In one example, the first and second continuous functions are polynomialfunctions, wherein each of the functions comprises a set of parameters,i.e. the first continuous function comprises a first set of parametersand the second continuous function comprises a second set of parameters.Likewise, a first set of parameter values is used to set the firstparameter set and a second set of parameter values is used to set thesecond parameter set. As indicated further above the first set ofparameter values can be determined by using a first layer of anartificial neural network and the second set of parameter values can bedetermined by using a second layer of the artificial neural network.

According to an embodiment the first continuous function and the secondcontinuous function are equivalent. For example, the first and secondcontinuous functions can both be a polynomial function, in particular offourth order. This can generally be expressed asp(x)=a*x⁴+b*x³+c*x²+d*x+e, wherein [a, b, c, d, e] is a set ofparameters of the continuous function p(x). The continuous variable xcan be the continuous-time variable. The set of parameter values [a, b,c, d, e] can be set according to a set of parameter values, e.g. [0.5,1, 3, 0.8, 0.5].

According to an embodiment the at least one parameter value comprises atleast one third parameter value, wherein a third continuous function andthe at least one third parameter value represent confidence values forthe expected trajectory of the object. The third continuous function cangenerally be a continuous function in addition to another continuousfunction (e.g. a first and/or second continuous function) representingthe trajectory of the object in a dimension. The other continuousfunction can be provided from an external source and the determining ofthe other continuous function and/or its parameter values is notnecessarily part of the method disclosed herein although this can bedone, if desired. For example, the parameter value for the thirdcontinuous function can be determined by the method disclosed herein.The parameter value and the third continuous function representconfidence information for another continuous function, wherein theother continuous function and its parameter values represent thetrajectory of an object and form an input for the machine-learningmodel. The third continuous function can be regarded as a supplementaryfunction that provides continuous confidence information on the expectedvalidity of another continuous function with its parameters set to thedetermined values. For example, the third function with its parametersset to the third parameter values can represent the expected errorbetween the other continuous function and the true trajectory of anobject. This can represent the propagated error (or uncertainty) withrespect to the parameters of the first and/or second continuousfunction. The third continuous function allows for an adaptiveprocessing of the information on the expected trajectory, i.e.processing on the basis of the third continuous function and the atleast one third parameter value. If, for example, the confidence is lowthe influence of the expected trajectory on the generation of automaticsteering commands can be reduced, possibly up to zero.

The confidence represented by the third continuous function can refer tothe first and/or second continuous function. This means that theprobabilistic or expected validity of the first and/or second continuousfunction is captured by the third continuous function (including therespective parameter values).

According to an embodiment at least one confidence value is generatedfor the at least one parameter value. For example, a confidence valuecan be generated for each of a plurality of parameter values,respectively, for example for the first and/or second continuousfunction. A confidence value can be provided per parameter. This enablesuncertainty propagation so that the expected error (or uncertainty) canbe provided for the one or more continuous functions. The at least oneconfidence value can be generated by using the machine-learning model,in particular neural network, which is used for determining the one ormore parameters. The at least one parameter determined by the machinelearning model can be formed by the at least one confidence value.Multivariate Gaussian variances can be used to generate the confidencevalue, wherein a negative log-likelihood formulation can be used forestimation. Proper propagation of the confidence value can then becarried out with respect to the continuous function.

In general, at least some steps of the method described herein can berepeated, for example during subsequent points in times. This allows forobtaining the information on the expected trajectory in a time-adaptivemanner, e.g. the expected trajectory can be updated, which can be doneregularly with a repetition rate, in particular periodically.

According to an embodiment at least some steps of the method arerepeatedly carried out with a repetition rate, wherein the repetitionrate is varied on the basis of a confidence value for the expectedtrajectory of the object (e.g., as represented by the third continuousfunction and the at least one third parameter) and/or a confidence valuefor the at least one parameter value (e.g., the generated confidencevalue). For example, portions of relatively high confidence can bedetected (e.g., in the third continuous function), wherein therepetition rate can be reduced during detected portions of relativelyhigh confidence. In this way, the processing effort can be reducedduring time periods, which do not require a frequent update of theexpected trajectory due to the relatively high confidence.

In general, a confidence value can represent a standard deviation or avariance of an expected error distribution.

According to an embodiment determining the at least one parameter valuedoes not comprise interpolating between data points of the expectedtrajectory of the object at the second time. The continuous functionrepresents a true prediction that is distinguished from an ex-postinterpolation between given data samples. Interpolation is thus regardedas no information gain compared to the samples alone. In contrast, thecontinuous function provides true continuous information for anarbitrary value of the continuous-time variable.

It is understood that the method can be carried out for a plurality ofobjects, i.e. the individual expected trajectories of the objects can bedetermined. This can be done on the basis of the same data, whichcaptures the plurality of objects and/or their vicinity. Possibleinteractions between objects can then be considered effectively, hencefurther improving the accuracy of the determined information on theexpected trajectory.

In another aspect, the present disclosure is directed at acomputer-implemented method for training an artificial neural networkfor determining information on an expected trajectory of an object. Themethod comprises training the artificial neural network usingbackpropagation, wherein positions of the object are sampled at randomfrom training data, wherein a loss or error function is evaluated on thebasis of the sampled positions, wherein function values of thecontinuous function are evaluated for sample values of the continuousfunction. The sample values are associated with the sample positions.Overfitting of the continuous function can thus be avoided. It isunderstood that the training data and/or the continuous function can beused in different representations, if desired. For example, thecontinuous function or portions of the continuous function can betransformed into different coordinate systems or otherwise adapted tofacilitate comparison with the training data. Training data can compriseknown continuous functions, which facilitates training even further.

In another aspect, the present disclosure is directed at a computersystem, said computer system being configured to carry out several orall steps of the computer-implemented method described herein.

The computer system may comprise a processing unit, at least one memoryunit and at least one non-transitory data storage. The non-transitorydata storage and/or the memory unit may comprise a computer program forinstructing the computer to perform several or all steps or aspects ofthe computer implemented method described herein.

In another aspect, the disclosure is directed at a vehicle comprising atleast one sensor for determining sensor data, the sensor datarepresenting a vicinity of the vehicle with at least one moveable objectlocated in the vicinity, wherein the vehicle further comprises acomputer system receiving the sensor data from the at least one sensorand determining an expected trajectory of the at least one object on thebasis of the sensor data. The computer system can be configured to carryout several or all steps of the computer-implemented method describedherein. While sensor data of the vehicle can be a primary source of datait is possible to use other data sources as well. For example, thevehicle can comprise a communication device for communicating with othervehicles in the vicinity, thus forming a communication network withother vehicles. The respective vehicle, which may be denoted as hostvehicle, can then receive sensor data from other vehicles via thecommunication network, e.g. motion data of other vehicles. This data canbe processed together with the sensor data of the host vehicle in orderto increase the validity of the information on the expected trajectory.In the same manner, other types of data, for example concerning theenvironmental conditions, can be used also.

In another aspect, the disclosure is directed at a computer-implementedmethod for controlling a vehicle on the basis of information on anexpected trajectory, the method comprising: determining the informationon the expected trajectory of at least one object located in a vicinityof the vehicle; and controlling the vehicle on the basis of theinformation on the expected trajectory of the at least one object. Theinformation can be determined on the basis of data, in particular sensordata, as described further above. Likewise, other features andembodiments described herein can be part of the method. For example, theinformation on the expected trajectory can comprise the first, secondand/or third continuous function and the assigned parameter values orsets of parameter values. It is possible that only the parameter valuesare determined, wherein the continuous functions are stored in a storagedevice of the computer system.

In another aspect, the present disclosure is directed at anon-transitory computer readable medium comprising instructions forcarrying out several or all steps or aspects of the computer-implementedmethods described herein. The computer readable medium may be configuredas: an optical medium, such as a compact disc (CD) or a digitalversatile disk (DVD); a magnetic medium, such as a hard disk drive(HDD); a solid state drive (SSD); a read only memory (ROM), such as aflash memory; or the like. Furthermore, the computer readable medium maybe configured as a data storage that is accessible via a dataconnection, such as an internet connection. The computer readable mediummay, for example, be an online data repository or a cloud storage. Itcan be connected to a vehicle in order to carry out the method for oneor more objects in the vicinity of the vehicle.

The present disclosure is also directed at a computer program forinstructing a computer to perform several or all steps or aspects of thecomputer-implemented methods described herein.

BRIEF DESCRIPTION OF THE DRAWINGS

Exemplary embodiments and functions of the present disclosure aredescribed herein in conjunction with the following drawings, showingschematically:

FIG. 1 a road with a plurality of moving objects, wherein an expectedtrajectory is determined for one of the objects;

FIG. 2 steps of a method for determining an expected trajectory of anobject.

DETAILED DESCRIPTION

Given the above background, there is a need to provide an improvedmethod for determining information on the expected trajectory of anobject.

FIG. 1 depicts a road 10, which is curved. Other shapes of roads arepossible, i.e. the road 10 is just an example. A plurality of objects12, 14, 16, 18, 20 are moving on the road 10 from the left side to theright side of FIG. 1. The objects 12, 14, 16, 18, 20 represent vehicles,for example passenger cars. While the objects 12 to 20 are alltravelling along the road 10 in roughly the same direction theindividual motion of the objects 12 to 20 can vary between the objects,for example with respect to velocity and current steering direction.This is to say that the trajectories of the individual objects 12 to 20are different.

The vehicle 16 comprises a sensor unit 22 for determining sensor data,which captures a vicinity around the vehicle 16. In this regard, thesensor unit 22 has range 24, wherein the objects 12 to 20 are all atleast partially within the range 24. The sensor data is determined at apredefined repetition rate so that the sensor unit 22 provides datarepresenting the current vicinity, in particular the position and/ormotion of the objects 12 to 20. This is to say that the sensor unit 22provides data about all the objects 12 to 20 including object 16 onwhich the sensor unit 22 is mounted. Preferably, the sensor unit 22includes a plurality of sensors with at least partially different fieldof views. In this way, the data provides an all-round visibility withrespect to object 16. However, other configurations are possible and therange 24 can have a different shape than shown in FIG. 1. It isunderstood that different data types, for example camera images, radarscans, LiDAR scans and the like can be fused in order to obtain aunified spatial representation of the current vicinity of the object 16.

The data determined by the sensor unit 22 is related to the expectedtrajectory of the object 18. This is due to the assumption that thetrajectory of the object 18 is influenced by at least some of theobjects, for example object 16, object 20, and object 18. In oneexample, the data determined by the sensor unit 22 represents thevicinity 24 at a first time, in particular during a first time period. Afirst trajectory 26 is shown as a dotted line in FIG. 1 and representsthe trajectory of the object 18, which has been measured on the basis ofthe sensor data. The sensor data is then processed by the computersystem (not shown) in order to determine the expected trajectory 28,which the object 18 is expected to move along during the second timeperiod, which is subsequent to the first time period. The second orexpected trajectory 28 is determined by a first set of parameter valuesfor a first continuous function and a second set of parameter values fora second continuous function. In addition, a third set of parametervalues is determined for a third continuous function, which representsthe confidence of the first continuous function and the secondcontinuous function including their assigned parameter values. Asconfidence, multivariate Gaussian variances can be used, and itsestimation can be formulated using negative log-likelihood, whereinerror propagation can be carried out with respect to the first and/orsecond continuous function. In this way, continuous information on theexpected trajectory 28 is provided, which is indicated by a continuousline in FIG. 1.

The object 16 is an autonomous vehicle comprising a computer system forgenerating suitable control actions so that the vehicle navigatessafely, for example on the road 10 as shown in FIG. 1.

Determining the first, second and third continuous function includingtheir parameter values is now described in greater detail in connectionwith FIG. 2. In step 30, sensor data 32 is determined, wherein thesensor data 32 is related to the expected trajectory 28 of the object18. In step 34, a plurality of parameter values are determined for acontinuous function, wherein the parameter values and the continuousfunction represents continuous information on the expected trajectory28. The first continuous function is a fourth-order polynomial function,namely f(t)=a*t⁴+b*t³+c*t²+d*t+e, wherein [a, b, c, d, e] is the firstset of parameters and t is a continuous-time variable. It is understoodthat t can be set to any desired real value within a definition range,hence t is not limited to discrete time instances in the sense that thefunction f(t) can be evaluated for any real value of t. The first set ofparameters are set to a first set of parameter values, e.g. [a=2, b=4,c=3, d=1, e=0]. The first function f(t) describes the trajectory 28 in afirst spatial dimension, e.g. the x-dimension of a Cartesian coordinatesystem. This is, f(t) gives the expected x-coordinate position of theobject 18 for a given value of t. Likewise, the second continuousfunction gives the expected y-coordinate position of the object 18 for agiven value of t. In more general terms, the second continuous functiondescribes the trajectory 28 in a second spatial dimension. The secondcontinuous function is g(t)=i*t⁴+j*t³+k*t²+1*t+r, wherein [i, j, k, l,r] is the second set of parameters, which are set to the second set ofparameter values, e.g. [i=1, j=2, k=4, 1=3, r=0]. It is understood thatthe position of the object 18 can now be described by a coordinate pair[f(t); g(t)] for a given value of t and with the parameters being set tothe assigned parameter values. It is further understood that thefunctions f(t) and g(t) are equivalent due to the same mathematicalstructure.

The confidence in the first and second sets of parameter values for thefirst and second continuous functions is expressed by a third set ofparameter values for the third continuous function, e.g.h(t)=m*t⁴+n*t³+o*t²+p*t+q, wherein [m, n, o, p, q] is the third set ofparameters, which are set to the third set of parameter values, e.g.[m=1, n=1, o=2, p=5, q=0]. In one example, the rate at which the steps30 and 34 are carried out is varied on the basis of the third continuousfunction and the third set of parameter values, for example if loss(t)is low, e.g. below a predefined threshold, the repetition rate can bereduced by an offset or factor, wherein otherwise the repetition rate isnot reduced. This can be carried out in dependence oft, wherein anaverage value can be determined for a given range oft.

What is claimed is:
 1. A computer-implemented method for determiningcontinuous information on an expected trajectory of an object, thecomputer-implemented method comprising: obtaining sensor data related tothe expected trajectory of the object, the sensor data representing avicinity of a vehicle with the object located in the vicinity; anddetermining, based on the sensor data, at least one parameter value fora continuous function, the continuous function and the at least oneparameter value representing the continuous information on the expectedtrajectory of the object, the at least one parameter value determined byusing a machine-learning model.
 2. The computer-implemented method ofclaim 1, wherein the sensor data is determined at a first time, thesensor data being related to the expected trajectory of the object at asecond time, wherein the continuous function and the at least oneparameter value represent the continuous information on the expectedtrajectory of the object at the second time.
 3. The computer-implementedmethod of claim 1, wherein the continuous function represents a functionvalue based on a continuous variable and at least one additionalparameter, the at least one additional parameter set based on the atleast one parameter value.
 4. The computer-implemented method of claim1, wherein the machine-learning model comprises an artificial neuralnetwork.
 5. The computer-implemented method of claim 4, wherein theartificial neural network comprises a plurality of layers, wherein theat least one parameter value comprises a plurality of secondaryparameter values, wherein at least some of the plurality of secondaryparameter values are respectively determined by a respective one of theplurality of layers of the artificial neural network or wherein at leastsome of the plurality of secondary parameter values are determined by asingle layer of the artificial neural network.
 6. Thecomputer-implemented method of claim 1, wherein the continuous functionforms a polynomial function.
 7. The computer-implemented method of claim6, wherein the polynomial function has a degree of at least one.
 8. Thecomputer-implemented method of claim 1, wherein the at least oneparameter value comprises at least one first parameter value and atleast one second parameter value, wherein a first continuous functionand the at least one first parameter value represent the expectedtrajectory of the object in a first dimension, wherein a secondcontinuous function and the at least one second parameter valuerepresent the expected trajectory of the object in a second dimension.9. The computer-implemented method of claim 8, wherein the at least oneparameter value further comprises at least one third parameter value,wherein a third continuous function and the at least one third parametervalue represent confidence values for the expected trajectory of theobject.
 10. The computer-implemented method of claim 1, wherein at leastone confidence value is generated for the at least one parameter value.11. The computer-implemented method of claim 1, wherein at least somesteps of the computer-implemented method are repeatedly carried out witha repetition rate, wherein the repetition rate is varied based on afirst confidence value for the expected trajectory of the object or asecond confidence value for the at least one parameter value.
 12. Thecomputer-implemented method of claim 1, wherein determining the at leastone parameter value does not comprise interpolating between data pointsof the expected trajectory of the object.
 13. A system comprising: atleast one sensor for determining sensor data, the sensor datarepresenting a vicinity of a vehicle with at least one object located inthe vicinity; and one or more processors configured to: obtain thesensor data related to an expected trajectory of the object; anddetermine, based on the sensor data, at least one parameter value for acontinuous function, the continuous function and the at least oneparameter value representing continuous information on the expectedtrajectory of the object, the at least one parameter value determined byusing a machine-learning model.
 14. The system of claim 13, wherein thesensor data is determined at a first time, the sensor data being relatedto the expected trajectory of the object at a second time, wherein thecontinuous function and the at least one parameter value represent thecontinuous information on the expected trajectory of the object at thesecond time.
 15. The system of claim 13, wherein the continuous functionrepresents a function value based on a continuous variable and at leastone additional parameter, the at least one additional parameter setbased on the at least one parameter value.
 16. The system of claim 13,wherein the machine-learning model comprises an artificial neuralnetwork.
 17. The system of claim 16, wherein the artificial neuralnetwork comprises a plurality of layers, wherein the at least oneparameter value comprises a plurality of secondary parameter values,wherein at least some of the plurality of secondary parameter values arerespectively determined by a respective one of the plurality of layersof the artificial neural network or wherein at least some of theplurality of secondary parameter values are determined by a single layerof the artificial neural network.
 18. The system of claim 13, whereinthe at least one parameter value comprises at least one first parametervalue and at least one second parameter value, wherein a firstcontinuous function and the at least one first parameter value representthe expected trajectory of the object in a first dimension, wherein asecond continuous function and the at least one second parameter valuerepresent the expected trajectory of the object in a second dimension.19. The system of claim 18, wherein the at least one parameter valuefurther comprises at least one third parameter value, wherein a thirdcontinuous function and the at least one third parameter value representconfidence values for the expected trajectory of the object.
 20. Anon-transitory computer-readable medium comprising computer-executableinstructions that, when executed, cause a processor to: obtain sensordata related to an expected trajectory of an object, the sensor datarepresenting a vicinity of a vehicle with the object located in thevicinity; and determining, based on the sensor data, at least oneparameter value for a continuous function, the continuous function andthe at least one parameter value representing continuous information onthe expected trajectory of the object, the at least one parameter valuedetermined by using a machine-learning model.